A Probabilistic Distance-Based Stability Quantifier for Complex Dynamical Systems
arXiv (Cornell University)(2023)
摘要
For a dynamical system, an attractor of the system may represent the
`desirable' state. Perturbations acting on the system may push the system out
of the basin of attraction of the desirable attractor. Hence, it is important
to study the stability of such systems against reasonably large perturbations.
We introduce a distance-based measure of stability, called `stability bound',
to characterize the stability of dynamical systems against finite
perturbations. This stability measure depends on the size and shape of the
basin of attraction of the desirable attractor. A probabilistic sampling-based
approach is used to estimate stability bound and quantify the associated
estimation error. An important feature of stability bound is that it is
numerically computable for any basin of attraction, including fractal basins.
We demonstrate the merit of this stability measure using an ecological model of
the Amazon rainforest, a ship capsize model, and a power grid model.
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关键词
stability quantifier,dynamical systems,distance-based
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